In predictive analytics, accuracy may not be a reliable metric for characterizing performance of a predictive algorithm. This is because accuracy can yield misleading results, particularly to a non-expert business user and particularly where the data set is unbalanced or cost of error of false negatives and false positives is mismatched. An unbalanced dataset can be one in which the numbers of observations in different classes vary. For example, if there were 95 cats and only 5 dogs in the data, a particular classifier might classify all the observations as cats. The overall accuracy would be 95%, but the classifier would have a 100% recognition rate (e.g., true positive rate, sensitivity) for the cat class but a 0% recognition rate for the dog class.